We add production-grade AI agents and automation to the software and workflows you already run — designed with your people, hardened for real customers, and yours to keep.
Four disciplines, sold two ways: as eleven fixed-scope packages with a written deliverable, or as custom consultancy when your problem doesn’t fit a box. Both start inside a tool you already use and end with a deployment your team can run without us.
Put agents where your users already are: a plain-English query bar, an assistant that edits the dashboard, a workflow that finishes itself. Multi-agent orchestration, model routing, structured outputs, streaming UIs.
Email, Slack, WhatsApp, calendar, CRM, spreadsheets. Agents do the routine 80%, stage anything risky for a person to approve, and leave an audit trail.
Answers grounded in your contracts, manuals, tickets and live data — every claim linked to where it came from, and “not covered” instead of a confident guess.
Already have AI in production and it’s flaky or expensive? We add a release gate of graded scenarios, enforce agent permissions in infrastructure (not prompts), trace every run, and cut spend with context engineering.
Written deliverables, a delivery window, set iteration rounds and a support period. You know what you’re getting before you commit.
Browse the catalog Lane 02 · Anything elseAny AI or full-stack system, scoped in writing within 48 hours. Build sprints, fractional AI engineering, embedded delivery, advisory.
See how custom work runsNot sure which one you need? Run the scope matcher — four questions, twenty seconds.
Most “AI” gets bolted on as a chat box nobody opens. We build the layer that sits between your people and your tools: it reads what arrives, does what’s routine, asks when it shouldn’t decide, and writes back to the systems you already trust.
Your existing REST APIs, databases and SaaS become agent tools — no rip-and-replace, no new “platform” to migrate to.
Intake, triage, drafting, reconciliation, reporting. The repetitive 80% runs on its own; exceptions get routed to a person.
Permissions enforced at the gateway, evals before every release, a full trace of every run. Safe enough for regulated data.
The same low-risk path every time: we sit with your team, pick one high-ROI workflow, prove it against your real data, then harden it. You see a live result before you commit to more.
First live win in 2–3 weeks. Most teams expand from there.
We shadow the real work — the handoffs, the exceptions, the spreadsheet nobody admits to. We leave with one workflow worth automating first and a clear definition of “done”.
A working agent on your actual documents, tickets or database — not a slide deck. Your team uses it, breaks it, and tells us what’s wrong while it’s cheap to change.
Graded eval scenarios as the release gate, permissions enforced at the gateway, human approval for risky actions, full traces of every run. Then we ship it into the tool your team already lives in.
Add the next workflow, wire in more tools, train your team. Everything runs in your cloud, in your repo, under your name. Keep us on retainer or don’t — no lock-in either way.
A few of the systems our founder has shipped in production — and the patterns we now build for clients. Open any to see how it works.
Non-technical teams build charts and query data in plain English. Agents generate schema-constrained chart configs, so every output is valid by construction.
Every answer links its exact source; the system says “not covered” rather than inventing an answer — the bar enterprises need before rollout.
76 tools over 9 business domains, with a memory system that keeps per-request cost flat as history grows.
RAG pipeline shaped around how clinicians actually search, with evidence-based recommendations inside their daily workflow.
Context engineering: strip tool-schema noise before dispatch, summarise stale tool results out of history, route cheap models to cheap tasks.
A hermetic eval harness runs hundreds of graded scenarios against an in-process fake of the production API — one CI pass/fail.
Agentry is founder-led. You work directly with an engineer who has spent seven years shipping production software and the last three building LLM and multi-agent systems that real users depend on — from clinicians at a Fortune 500 healthcare company to business teams querying data in plain English every day.
The whole path, owned end to end: sit with the customer, learn the domain and the data, prototype against the actual workflow, then harden it into a governed, observable deployment — and stay on after go-live.
An agent is only worth anything if it can reach the systems the work actually lives in. Yours answers in Slack, reads the shared inbox, updates the CRM and closes the ticket — through your existing accounts, under your existing permissions. No migration, and no new dashboard nobody opens.
Every connection is a tool the agent is explicitly granted — scoped, logged and revocable, never a shared password. Anything with an API can become one, so if a system you rely on isn’t here, wiring it up is usually a day, not a project.
If manual work, a flaky AI feature, or a workflow that only lives in someone’s head is limiting growth, we should talk. Start with one high-ROI win — no big commitment.